Artificial Intelligence (AI) is transforming the landscape of pathology, particularly in resource-constrained settings like the Philippines. This narrative review explores the applications, challenges, and future potential of AI in digital image analysis for pathology practices. By synthesizing peer-reviewed literature from 2019 to 2024, the review highlights the role of machine learning (ML) and deep learning (DL) algorithms in enhancing diagnostic accuracy, workflow efficiency, and clinical decision-making. AI-driven tools such as convolutional neural networks (CNNs) and transfer learning models have demonstrated significant success in tumor detection, biomarker evaluation, and predictive analytics, paving the way for personalized medicine. However, barriers such as limited annotated datasets, privacy concerns, and model interpretability hinder widespread adoption. The review emphasizes the need for ethical frameworks, workforce training, and infrastructure development to ensure equitable and effective integration of AI into pathology practices. By addressing these challenges, AI has the potential to improve diagnostic precision, expand access to healthcare, and modernize pathology services in the Philippines.